All-Polyethylene Tibial Components in Total Knee Replacement: Early Failures
Bibliographic record
Abstract
Abstract In total primary knee replacement surgery, the use of all-polyethylene tibial (APT) components has many advantages, including no backside wear and no linear dissociation. In addition, the greater polyethylene thickness permits more conservative bone resection compared with that for metal-backed components, with a lower unit cost and similar functional results. Thus, the use of an APT in primary total knee arthroplasty remains an attractive option. This is a review of 158 patients who underwent primary knee replacement using APT components from a single manufacturer. Data collection included age, American Society of Anesthesiology physical status classification, body mass index (BMI), type of deformity, the presence of diabetes mellitus, rate of revision, and characteristics associated with early failure of the components. Average follow-up time was 40 months. The revision rate for any reason was 5.6%, and the average BMI in revision cases was 37.6. Patients with a higher BMI (≥ 37.6) were significantly more likely to require revision surgery than patients with a lower BMI (p = 0.04). In our sample, high BMI was a contributing factor for early failures in total knee replacements using an APT component. Generally, polyethylene tibial components used for primary knee replacements are safe and effective, with good outcomes and subsequent lower costs to the health care system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".